PROJECT SUMMARY Cardiovascular disease is the leading cause of death globally and has several risk factors including high blood lipid levels, insulin resistance, hypertension, and obesity. Genome-wide association studies (GWAS) have identified hundreds of genetic risk variants for cardiovascular disease and related traits, including cholesterol, triglycerides, atherosclerosis, insulin sensitivity, and body fat distribution, the majority of which are found in non- protein-coding genomic regions, contain multiple genetic variants that are in high linkage disequilibrium (LD) with each other, and may have functional roles in one or more tissues. Therefore, identifying the causal genetic variant(s) at a GWAS locus, the relevant tissue(s), and the molecular mechanisms used by these variants to impact a trait is challenging. Subcutaneous adipose tissue (SAT) regulates cardiovascular-relevant traits through its roles in lipid storage, insulin signaling, and hormone secretion. Many GWAS loci for cardiovascular disease and relevant metabolic traits contain variants that are also associated with SAT gene expression, termed quantitative trait loci (eQTLs), but the mechanisms by which these variants alter gene expression are largely uncharacterized. GWAS variants for multiple traits are enriched in transcriptional regulatory elements of relevant cell types, which are marked by chromatin accessibility. Further, chromatin accessibility quantitative trait loci (cQTLs) have been identified in a few cell types, and chromatin accessibility has been shown to mediate genetic effects on gene expression. Therefore, I hypothesize that modulation of SAT chromatin accessibility is a common mechanism by which cardiovascular GWAS variants impact gene expression and disease. To identify a robust set of SAT transcriptional regulatory elements, I will profile chromatin accessibility in SAT samples from 400 individuals in the METabolic Syndrome in Men (METSIM) study, which has existing dense genotype, gene expression, and cardiovascular-relevant trait measurements. I will identify genetic variants associated with SAT chromatin accessibility using QTL mapping and allelic imbalance (AI) analysis. Using the extensive genotype and phenotype measurements in METSIM, I will perform co-localization and causal inference tests to identify genetic variants that alter chromatin accessibility, subsequently alter gene expression, and ultimately impact disease-relevant traits. These results will help identify causal genetic variants at cardiovascular GWAS loci, target genes for these variants, and the noncoding elements that regulate transcription of these genes. cQTLs with predicted causal effects on disease can also be compared to the locations of transcription factor binding sites, chromatin contact data, and other genomic annotations to gain further mechanistic insight into how genetic variation alters transcription to impact disease. Characterizing the target genes and mechanisms of action of disease-associated genetic variants will guide the development of therapeutic strategies for cardiovascular disease.